Results 51 to 60 of about 24,139,341 (352)
A Hierarchical Framework for Correcting Under-Reporting in Count Data [PDF]
Journal of the American Statistical Association, 2018 Tuberculosis poses a global health risk and Brazil is among the top 20 countries by absolute mortality. However, this epidemiological burden is masked by under-reporting, which impairs planning for effective intervention.
O. Stoner +2 more
semanticscholar +1 more source
Modeling zero-inflated count data with glmmTMB
bioRxiv, 2017 Ecological phenomena are often measured in the form of count data. These data can be analyzed using generalized linear mixed models (GLMMs) when observations are correlated in ways that require random effects. However, count data are often zero-inflated,
Mollie E. Brooks +8 more
semanticscholar +1 more source
On Goodness-of-Fit Tests for the Neyman Type A Distribution
Revstat Statistical Journal, 2023 The two-parameter Neyman type A distribution is quite useful for modeling count data, since it corresponds to a simple, flexible and overdispersed discrete distribution, which is also zero[1]inflated.
Apostolos Batsidis, Artur J. Lemonte
doaj +1 more source
Generational Differences in Household Car Ownership
Journal of Sustainable Development of Energy, Water and Environment Systems, 2021 The stagnation of car demand had been observed in many countries. A similar phenomenon had emerged in Taiwan. From the perspective of socio-demographic characteristics, this study employs quantile regression for count data to investigate generational ...
Wen-Hsiu Huang, Ming-Che Chao
doaj +1 more source
Regression Models for Count Data in R
, 2008 The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing.
A. Zeileis +2 more
semanticscholar +1 more source
Ordinal regression models for zero-inflated and/or over-dispersed count data
Scientific Reports, 2019 Count data commonly arise in natural sciences but adequately modeling these data is challenging due to zero-inflation and over-dispersion. While multiple parametric modeling approaches have been proposed, unfortunately there is no consensus regarding how
D. Valle +3 more
semanticscholar +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT We sought to identify potential early risk biomarkers for lung disease in children post‐allogeneic HCT. Patients with pulmonary function tests 3 months post‐transplant and plasma samples between days 7 and 14 post‐HCT were included. Six of 27 subjects enrolled had reduced forced expiratory volume 1 (FEV1) z scores.
Isabella S. Small +3 more
wiley +1 more source
Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction
G3: Genes, Genomes, Genetics, 2016 Genomic tools allow the study of the whole genome, and facilitate the study of genotype-environment combinations and their relationship with phenotype. However, most genomic prediction models developed so far are appropriate for Gaussian phenotypes.
Abelardo Montesinos-López +8 more
doaj +1 more source
SPARSim single cell: a count data simulator for scRNA-seq data
Bioinform., 2019 MOTIVATION Single cell RNA-seq (scRNA-seq) count data shows many differences compared to bulk RNA-seq count data, making the application of many RNA-seq preprocessing/analysis methods not straightforward or even inappropriate.For this reason, the ...
Giacomo Baruzzo, I. Patuzzi, B. Camillo
semanticscholar +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Purpose Metabolic syndrome (MetS) is a common complication in survivors of childhood acute lymphoblastic and myeloid leukemia (AL), and a major risk factor for premature cardiovascular disease, type‐2‐diabetes, and metabolic dysfunction‐associated steatotic liver disease (MASLD).
Visentin Sandrine +10 more
wiley +1 more source